Currently the great majority of decisions in healthcare are made with an imperfect understanding of their consequences. At the individual level, physicians' perceptions of their patients' risks and the effects of treatments vary widely, with corresponding effects on practice patterns. At the population level, guidelines, performance measures, incentives, and disease management programs are launched with little if any knowledge of their potential effects.
The Archimedes Model, commercially available through professional services from Archimedes, Inc., San Francisco, Calif., is a well-validated, realistic simulation of human physiology and disease and healthcare systems. These characteristics enable the Model to support research and decision-making about healthcare systems and policy at a level of detail previously not possible.
The Archimedes Optimizer, commercially available from Archimedes, Inc., is a computer-based decision support tool designed to give doctors, care managers and patients an accurate individualized assessment of the health benefits of preventive pharmaceutical and behavioral interventions such as blood pressure medications or weight loss. The Archimedes Optimizer is based on the Archimedes Model and uses as input, patient or health plan member data including demographic information, biomarkers, medication history, and behaviors which are extracted from the electronic medical record or other similar databases. The Archimedes Optimizer's output is designed to be shared with the member as well as the healthcare provider.
Quantitative information about the current adverse health outcome risk and the risk reduction of specific interventions has not been available to either physicians or their patients before the Archimedes Optimizer. As a result of this lack of information, interventions are often not prescribed to patients who would benefit greatly from the intervention and prescribed to others who would benefit very little.
Even when the intervention is correctly prescribed, the lack of quantitative information makes it difficult for a medical practitioner to effectively convey intervention information to a patient, and efforts to do so may be misinterpreted by the patient. The result is sub-optimal health for a patient who, due to this misinterpretation, fails to act on a suggested intervention or misapplies the information provided.
Furthermore, the current methods used to convey the results of medical interventions, such as taking a particular drug or losing weight, are dependent on the knowledge of the doctor of the effects of the interventions and the interaction and overlap of the interventions for a person with characteristics that are similar to those of the patient. This reliance on the practitioner to be able to convey such details to the patient coupled with the possibility of misinterpretation by the patient exposes multiple degrees of human error capable of reducing the quality of life of the patient.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.